Your AI travel booking agent just charged your customer's credit card fourteen times for the same flight. The agent called the booking tool, got back a vague response, was not sure if it worked, and tried again. And again. And again. Fourteen tool calls where two would have been enough. Twenty-one seconds of wasted time. Hundreds of thousands of burned tokens. This is the repeated tool call problem, and it is far more common than you think. The root cause is surprisingly simple. Ambiguous tool feedback leaves agents uncertain whether an action succeeded or failed. Without clear completion signals, agents default to retrying, which is a rational response to uncertainty. Studies show an average of 3.2x overcalling when tools return unstructured or unclear responses. The problem compounds in multi-step workflows where each step's ambiguity cascades forward. Three complementary solutions address the problem at different layers. First, DebounceHook maintains a sliding window of recent tool calls, detecting and blocking duplicate invocations before they execute with configurable window size and similarity thresholds. Second, clear SUCCESS/FAILED states redesign tool responses with explicit status indicators that tell the agent unambiguously whether to proceed or retry, using structured response formats with action guidance. Third, LimitToolCounts provides hard ceiling enforcement via the HookProvider that caps the maximum number of calls to any specific tool, acting as a safety net when other approaches miss edge cases.

Outline: • The Token Waste Problem • DebounceHook - Detect and Block Duplicates • Clear SUCCESS/FAILED States - Prevention by Design • LimitToolCounts - Hard Ceiling Enforcement • Production Patterns and Wrap-Up